基于数据域描述的模糊临近支持向量机算法

摘要

To solve the over-fitting problems with support vector machine (SVM) for the outlier or noise,the characteristics of fuzzy support vector machine (FSVM) and proximal support vector machine (PSVM) are analyzed. Drawn on their advantages, namely, fuzzy membership and proximal hyper plane, a method based on support vector data domain (SVDD) in description is proposed. This method fully considers the relationship between the distances of each sample point to the center of each class and the contribution rate of each sample.The improved algorithm performs more clearly and precise. The analytical results show the algorithm with fuzzy membership degree has a higher recognition rate, but spents a greater amount of training time.%针对传统支持向量机由于样本中存在孤立点或噪声而导致的过学习问题,通过分析模糊支持向量机和临近支持向量机的特点,借鉴它们的优点:模糊隶属度和临近超平面,提出了一种数据处理方法.该方法考虑了样本点到类中心的距离与样本对分类的贡献率的关系.这种改进使分类更为清晰和准确.结果表明:采用新的模糊隶属度模糊临近支持向量机算法有较高的识别率,但也耗费了较多的训练时间.